What the data for this category looks like
Data sources for aviation airport investment research include Civil Aviation Administration public airspace notices, airport operation logs, flight schedule datasets, passenger throughput statistical reports, industry research reports, and real-time airspace traffic interface data. Update rhythms vary significantly: flight schedules update daily, airspace traffic data refreshes hourly, and financial reports and industry research reports are released quarterly or on an ad-hoc basis. Document structures include structured tables (with fields such as flight number, takeoff and landing time), semi-structured PDF research reports, and plain text notices. Field units include specialized civil aviation units such as passenger trips, flight sorties, hectopascals, and others.
What constraints these characteristics impose on the model access and configuration link
Multi-source heterogeneous data structures require the model access layer to support mixed configuration for structured data parsing and unstructured text adaptation. Data with different update rhythms requires configuring incremental embedding trigger rules to avoid repeated processing of real-time data. Specialized field units require embedding models to support semantic recognition of civil aviation industry terms and units, otherwise recall accuracy will be reduced. Real-time airspace traffic data requires model call latency to be controlled within a reasonable range to avoid impacting investment research decision efficiency.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
embedding_batch_size | 8-16 | Aviation airport investment research documents are mostly single-page structured tables, with moderate single-text length. Small-batch embedding reduces memory usage and failure rates |
load_balancer_strategy | round_robin_with_weight | Different models have varying parsing efficiency for structured flight data and unstructured research reports. Weight-based load distribution balances resource usage |
maxContext | 8000-12000 characters | Aviation airport industry research reports often contain long paragraphs of policy interpretations and airspace planning content. Sufficient context windows improve recall of relevant information |
api_request_timeout | 30-60 seconds | Queries for real-time airspace traffic data require waiting for backend interface responses. A timeout that is too short will interrupt valid requests |
rerank_top_k | Top 3-5 results | Aviation airport investment research data has strong correlation. Excessive recall increases context processing pressure, and a small number of highly relevant results meets analysis needs |
PARSE_STRUCTURED_TABLE | enabled | The data source contains a large number of structured flight schedules and throughput reports. Enabling structured parsing improves the semantic accuracy of embedding vectors |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- Phenomenon: Model interfaces return 503 Service Unavailable status codes during peak hours. Cause: No request-type-based load balancing strategy is configured. Structured flight data requests and unstructured research report requests are assigned to the same model node, leading to resource overload.
- Phenomenon: Recall accuracy for multilingual flight notices remains consistently low. Cause: Only embedding vectors for some documents are updated, and a full knowledge base re-embedding operation is not performed. Vector spaces of old and new embedding models do not match.
- Phenomenon: Model output still contains thought process content. Cause: The
no_thinkparameter is incorrectly configured in the general parameter layer of conversation requests, rather than in the dedicated configuration items for model calls.
How to confirm the configuration is complete
- Upload a single structured flight schedule document from the aviation airport category, and verify that the extracted fields after parsing match the preset fields.
- Initiate 10 concurrent model call requests, and confirm that all return status codes are 200 OK, with no overload-related errors.
- After replacing the embedding model, perform a full knowledge base re-embedding task, and monitor whether the task progresses normally to completion.
- After configuring the
no_thinkparameter, initiate a conversation, and confirm that the model output does not contain thought process content.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.